Understanding the function of effectors’ structure-based function and protein-protein interactions from the amino acid sequence by computational calculations.

Identifying fungal effector proteins and understanding their function is of great importance in efforts to control losses to plant diseases. Modern high-throughput sequencing technologies have facilitated the availability of several fungal genomes and 1000s of transcriptomes. There is minimal consensus over the annotation and functionality of effector proteins. With the characterization of avirulence (Avr) genes, criteria for computational prediction of effector proteins are becoming more efficient. The proposed objectives are – 1) Effector’s 3D structure prediction, involving I-TASSER for iterative protein structure assembly, and ab initio protein folding using QUARK platform. 2) COACH based effectors-ligand binding affinities site prediction. 3) biological function annotation and predictions of effector molecules from its 3D structural model. 4) alignment-free bioinformatics approaches to identify a protein with structural and functional similarity to the Mlp37347 and Mlp124357. 5) To understand the 3D structure of Mlp37347-GAD1 and Mlp124357-PDI complexes from the sequence; and their structure-based function annotation.

Faculty Supervisor:

Hugo Germain

Student:

Partner:

University of Michigan

Discipline:

Life Sciences

Sector:

Education

University:

Université du Québec à Trois-Rivières

Program: